Forest census allows getting precise data for logging planning and elaboration of the forest management plan. Species identification blunders carry inadequate forest management plans and high risks inside forest concessions. Hence, an identification protocol prevents the exploitation of non-commercial or endangered timber species. The current Peruvian legislation allows the incorporation of non-technical experts, called “materos”, during the identification. Materos use common names given by the folklore and traditions of their communities instead of formal ones, which generally lead to misclassifications. In the real world, logging companies hire materos instead of botanists due to cost/time limitations. Given such a motivation, we explore an end-to-end software solution to automatize the species identification. This paper introduces the Peruvian Amazon Forestry Dataset, which includes 59,441 leaves samples from ten of the most profitable and endangered timber-tree species. The proposal contemplates a background removal algorithm to feed a pre-trained CNN by the ImageNet dataset. We evaluate the quantitative (accuracy metric) and qualitative (visual interpretation) impacts of each stage by ablation experiments. The results show a 96.64% training accuracy and 96.52% testing accuracy on the VGG-19 model. Furthermore, the visual interpretation of the model evidences that leaf venations have the highest correlation in the plant recognition task.

森林普查可以获取准确的数据，以进行伐木计划和森林管理计划的拟订。物种识别错误导致森林管理计划不充分，森林特许权内存在高风险。因此，识别协议可防止对非商业或濒危木材物种的开发。秘鲁现行法律允许在身份识别过程中纳入非技术专家，称为“ materos”。马特罗斯（Materos）使用其民俗和社区传统赋予的通用名称，而不是正式的名称，这通常会导致分类错误。在现实世界中，由于成本/时间的限制，伐木公司雇用的是物资而不是植物学家。鉴于这种动机，我们探索了一种端到端软件解决方案来自动进行物种识别。本文介绍了秘鲁亚马逊林业数据集，其中包括来自十个最有利可图和最濒危的木材树种的59441片叶子样本。该提案设想了一种背景去除算法，以通过ImageNet数据集提供经过预训练的CNN。我们通过消融实验评估每个阶段的量化（准确性指标）和定性（视觉解释）影响。结果显示，在VGG-19模型上，训练准确度为96.64％，测试准确度为96.52％。此外，对模型的视觉解释表明，叶片脉脉在植物识别任务中具有最高的相关性。该提案设想了一种背景去除算法，以通过ImageNet数据集提供经过预训练的CNN。我们通过消融实验评估每个阶段的量化（准确性指标）和定性（视觉解释）影响。结果显示，在VGG-19模型上，训练准确度为96.64％，测试准确度为96.52％。此外，对模型的视觉解释表明，叶片脉脉在植物识别任务中具有最高的相关性。该提案设想了一种背景去除算法，以通过ImageNet数据集提供经过预训练的CNN。我们通过消融实验评估每个阶段的量化（准确性指标）和定性（视觉解释）影响。结果显示，在VGG-19模型上，训练准确度为96.64％，测试准确度为96.52％。此外，对模型的视觉解释表明，叶片脉脉在植物识别任务中具有最高的相关性。